• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.4
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• pp.750-769
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• 2015

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

• Smart Structures and Systems
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• v.31 no.2
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• pp.113-130
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• 2023

Hybrid simulation (HS) has attracted community attention in recent years as an efficient and effective experimental technique for structural performance evaluation in size-limited laboratories. Traditional hybrid simulations usually take deterministic properties for their numerical substructures therefore could not account for inherent uncertainties within the engineering structures to provide probabilistic performance assessment. Reliable structural performance evaluation, therefore, calls for stochastic hybrid simulation (SHS) to explicitly account for substructure uncertainties. The experimental design of SHS is explored in this study to account for uncertainties within analytical substructures. Both computational simulation and laboratory experiments are conducted to evaluate the pseudo-random Sobol sequence for the experimental design of SHS. Meta-modeling through polynomial chaos expansion (PCE) is established from a computational simulation of a nonlinear single-degree-of-freedom (SDOF) structure to evaluate the influence of nonlinear behavior and ground motions uncertainties. A series of hybrid simulations are further conducted in the laboratory to validate the findings from computational analysis. It is shown that the Sobol sequence provides a good starting point for the experimental design of stochastic hybrid simulation. However, nonlinear structural behavior involving stiffness and strength degradation could significantly increase the number of hybrid simulations to acquire accurate statistical estimation for the structural response of interests. Compared with the statistical moments calculated directly from hybrid simulations in the laboratory, the meta-model through PCE gives more accurate estimation, therefore, providing a more effective way for uncertainty quantification.

• Structural Engineering and Mechanics
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• v.86 no.6
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• pp.715-730
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• 2023

Broad studies have addressed the issue of structural element damage identification, however, rubber bearing, as a key component of load transmission between the superstructure and substructure, is essential to the operational safety of a bridge, which should be paid more attention to its health condition. However, regarding the limitations of the traditional bearing damage detection methods as well as few studies have been conducted on this topic, in this paper, inspired by the model updating-based structural damage identification, a two-stage bearing damage identification method has been proposed. In the first stage, we deduce a novel bearing damage localization indicator, called element relative MSE, to accurately determine the bearing damage location. In the second one, the prior knowledge of bearing damage localization is combined with sailfish optimization (SFO) to perform the bearing damage estimation. In order to validate the feasibility, a numerical example of a 5-span continuous beam is introduced, also the noise robustness has been investigated. Meanwhile, the effectiveness and engineering applicability are further verified based on an experimental simply supported beam and actual engineering of the I-40 Bridge. The obtained results are good, which indicate that the proposed method is not only suitable for simple structures but also can accurately locate the bearing damage site and identify its severity for complex structure. To summarize, the proposed method provides a good guideline for the issue of bridge bearing detection, which could be used to reduce the difficulty of the traditional bearing failure detection approach, further saving labor costs and economic expenses.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.4 s.74
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• pp.419-427
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• 2006

In this paper, an experimental real-time hybrid method, which implements the earthquake response control of a building structure with a TLD(Tuned Liquid Damper) by using only a TLD as an experimental part, is proposed and is experimentally verified through a shaking table test. In the proposed methodology, the whole building structure with a TLD is divided into the upper TLD and the lower structural parts as experimental and numerical substructures, respectively. The control force acting between their interface is measured with a shear-type load-cell which is mounted on the shaking table. The shaking table vibrates the upper experimental TLD with the response calculated from the numerical substructure, which is subjected to the excitations of the measured interface control force at its top story and an earthquake input at its base. The experimental results show that the conventional method, in which both a TLD and a building structure model are physically manufactured and are tested, can be replaced by the proposed methodology with a simple experimental installation and a good accuracy for evaluating the control performance of a TLD.

• Journal of the Earthquake Engineering Society of Korea
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• v.3 no.3
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• pp.9-20
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• 1999

A method is proposed to estimate the joint damages of a steel structure from modal data using the neural networks technique. The beam-to-column connection in a steel frame structure is represented by a zero-length rotational spring of the end of the beam element, and the connection fixity factor is defined based on the rotational stiffness so that the factor may be in the range 0~1.0. Then, the severity of joint damage is defined as the reduction ratio of the connection fixity factor. Several advanced techniques are employed to develop the robust damage identification technique using neural networks. The concept of the substructural indentification is used for the localized damage assessment in the large structure. The noise-injection learning algorithm is used to reduce the effects of the noise in the modal data. The data perturbation scheme is also employed to assess the confidence in the estimated damages based on a few sets of actual measurement data. The feasibility of the proposed method is examined through a numerical simulation study on a 2-bay 10-story structure and an experimental study on a 2-story structure. It has been found that the joint damages can be reasonably estimated even for the case where the measured modal vectors are limited to a localized substructure and the data are severely corrupted with noise.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.6
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• pp.499-512
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• 2016

The boundary reaction method(BRM) is a substructure time domain method, it removes global iterations between frequency and time domain analyses commonly required in the hybrid approaches, so that it operates as a two-step uncoupled method. The BRM offers a two-step method as follows: (1) the calculation of boundary reaction forces in the frequency domain on an interface of linear and nonlinear regions, (2) solving the wave radiation problem subjected to the boundary reaction forces in the time domain. In the time domain analysis, the near-field soil is modeled to simulate the wave radiation problem. This paper evaluates the performance of the BRM according to modeling extent of near-field soil for the nonlinear SSI analysis of base-isolated NPP structure. For this purpose, parametric studies are performed using equivalent linear SSI problems. The accuracy of the BRM solution is evaluated by comparing the BRM solution with that of conventional SSI seismic technique. The numerical results show that the soil condition affects the modeling range of near-field soil for the BRM analysis as well as the size of the basemat. Finally, the BRM is applied for the nonlinear SSI analysis of a base-isolated NPP structure to demonstrate the accuracy and effectiveness of the method.